ArticleNature communications2017
A general pharmacodynamic interaction model identifies perpetrators and victims in drug interactions.
Article in Nature communications, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 53 papers.
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Who cites it
53 citing papers in PubMed.
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- How Advanced Artificial Intelligence Technologies Shape Drug-Drug and Drug-Target Interaction Modeling.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026Review
- Population pharmacokinetic modeling of sulfadimethoxine, sulfadiazine and sulfamethoxazole combined to trimethoprim in pigs.The veterinary quarterly · 2025Article
- Challenges to determine synergistic drug interactions in mice.Nature communications · 2025Article
- PKPD modeling of the inoculum effect of combined ceftazidime/avibactam and colistin against KPC-3Antimicrobial agents and chemotherapy · 2025Article
- Drug Interaction Studies of Cabamiquine:Ganaplacide Combination against HepaticACS infectious diseases · 2025Article
- Optimizing drug synergy prediction through categorical embeddings in deep neural networks.Biology methods & protocols · 2025Article
- Food interactions with tyrosine kinase inhibitors used to treat advanced renal cell carcinoma.Contemporary oncology (Poznan, Poland) · 2025Review
- Cardiotoxicity evaluation of two-drug fixed-dose combination therapy under CiPA: a computational study.Translational and clinical pharmacology · 2024Article
- Towards clinically relevant dose ratios for Cabamiquine and Pyronaridine combination using P. falciparum field isolate data.Nature communications · 2024Article
- Dose optimization of TBI-223 for enhanced therapeutic benefit compared to linezolid in antituberculosis regimen.Nature communications · 2024Article
- Pharmacokinetics-Pharmacodynamics Modeling for Evaluating Drug-Drug Interactions in Polypharmacy: Development and Challenges.Clinical pharmacokinetics · 2024Review
- DRGATAN: Directed relation graph attention aware network for asymmetric drug-drug interaction prediction.iScience · 2024Article
- Revisiting the Checkerboard to Inform Development of β-Lactam/β-Lactamase Inhibitor Combinations.Antibiotics (Basel, Switzerland) · 2024Article
- Two-way pharmacodynamic modeling of drug combinations and its application to pairs of repurposed Ebola and SARS-CoV-2 agents.Antimicrobial agents and chemotherapy · 2024Article
- Drug-drug interactions prediction based on deep learning and knowledge graph: A review.iScience · 2024Review
- Pharmacokinetic/pharmacodynamic analysis of ceftazidime/avibactam and fosfomycin combinations in anMicrobiology spectrum · 2024Article
Corrections and comments
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Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Assessment of pharmacodynamic (PD) drug interactions is a cornerstone of the development of combination drug therapies. To guide this venture, we derive a general pharmacodynamic interaction (GPDI) model for ≥2 interacting drugs that is compatible with common additivity criteria. We propose a PD interaction to be quantifiable as multidirectional shifts in drug efficacy or potency and explicate the drugs' role as victim, perpetrator or even both at the same time. We evaluate the GPDI model against conventional approaches in a data set of 200 combination experiments in Saccharomyces cerevisiae: 22% interact additively, a minority of the interactions (11%) are bidirectional antagonistic or synergistic, whereas the majority (67%) are monodirectional, i.e., asymmetric with distinct perpetrators and victims, which is not classifiable by conventional methods. The GPDI model excellently reflects the observed interaction data, and hence represents an attractive approach for quantitative assessment of novel combination therapies along the drug development process.
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